Abstract

The mathematization of science is undergoing a structural transformation driven by the rise of computation and data-intensive methods. While classical mathematization relied largely on explicitly formulated laws and formal structures, contemporary scientific practice increasingly encounters mathematical objects that arise from dynamical and algorithmic processes. This paper introduces the notion of computationally emergent structures to describe objects, representations, or effective functional spaces that are generated and stabilized through the interaction of parameterized models, optimization dynamics, and data. We propose a minimal formal schema in which such structures can be understood as stabilized outcomes of learning dynamics. In overparameterized regimes, this schema clarifies how optimization procedures may select particular solutions through implicit biases or variational tendencies that are not specified a priori. framework brings together implicit regularization, kernel regimes, and stability phenomena in modern learning systems, while distinguishing between established formal results and broader epistemological interpretation. It suggests that contemporary learning systems provide a privileged setting in which geometry, dynamics, and data jointly contribute to the production of effective mathematical structure. This perspective identifies a shift from representation to dynamical emergence and extends the study of mathematization toward an analysis of structure formation grounded in computation.

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